SOP: ARA Rigor Review
SkillDev toolsSOP: Run the external ARA rigor-reviewer (Seal Level 2, six-dimension semantic review) over ../ara/ and pass its level2_report.json to the user
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the SOP: ARA Rigor Review skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/ara-rigor-review/SKILL.md and read by ahel’s review.
Key question: 这份 ARA 的认识论严谨度如何?逻辑弧在结构上闭合了吗?
Preflight
先确认外部 rigor-reviewer skill 可 load。不可用则提示安装并停下。
Procedure
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跑 Level 2:
Skillload rigor-reviewer,传<artifact_dir>=../ara/。 它对 ARA 跑六维语义审查(全是要读懂 + 推理的语义检查,不是结构校验):- D1 Evidence Relevance — 证据是否在实质上支撑每条 claim;
- D2 Falsifiability Quality — 证伪标准是否有意义、可操作、范围合适;
- D3 Scope Calibration — claim 是否恰好断言其证据所支撑的,不多不少;
- D4 Argument Coherence — 是否从 problem→solution→evidence 逻辑闭合;
- D5 Exploration Integrity — exploration tree 是否记录了真实研究过程(含失败);
- D6 Methodological Rigor — 实验设计/baseline/ablation/报告是否到位。
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产物:
rigor-reviewer在 artifact 根目录写level2_report.json(每维 1–5 分 + strengths/weaknesses/suggestions + severity 排序 findings + overall grade + 给作者的问题)。 -
D5 低分不是错误,是"探索素材不足"信号。 透传给用户,由用户决定是否回
context-exploring补打捞过程线。本 SOP 不自动循环。
注意:
rigor-reviewer的 D1–D6 是 ARA 自己的维度,与 DARE 的 D1–D5 评判 标准是两套东西,不要混。本 SOP 只透传 ARA 的报告,不施加 DARE 的 D1–D5。
Output
ara/level2_report.json + 一句话总结(grade + 最该关注的 finding),交付用户。
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ara-rigor-review- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine